Econometric inconsistencies

16 Jun, 2018 at 10:17 | Posted in Statistics & Econometrics | 1 Comment

In plain terms, it is evident that if what is really the same factor is appearing in several places under various disguises, a free choice of regression coefficients can lead to strange results. It becomes like those puzzles for children where you write down your age, multiply, add this and that, subtract something else, and eventually end up with the number of the Beast in Revelation.

Prof. Tinbergen explains that, generally speaking, he assumes that the correlations under investigation are linear … One would have liked to be told emphatically what is involved in the assumption of linearity. It means that the quantitative effect of any causal factor on the phenomenon under investigation is directly proportional to the factor’s own magnitude … But it is a very drastic and usually improbable postulate to suppose that all economic forces are of this character, producing independent changes in the phenomenon under investigation which are directly proportional to the changes in themselves ; indeed, it is ridiculous. Yet this is what Prof. Tinbergen is throughout assuming …

J M Keynes

Keynes’ comprehensive critique of econometrics and the assumptions it is built around — completeness, measurability, independence, homogeneity, and linearity — is still valid today.

Most work in econometrics is made on the assumption that the researcher has a theoretical model that is ‘true.’ But — to think that we are being able to construct a model where all relevant variables are included and correctly specify the functional relationships that exist between them, is not only a belief without support, it is a belief impossible to support.

The theories we work with when building our econometric regression models are insufficient. No matter what we study, there are always some variables missing, and we don’t know the correct way to functionally specify the relationships between the variables.

Every econometric model constructed is misspecified. There is always an endless list of possible variables to include, and endless possible ways to specify the relationships between them. So every applied econometrician comes up with his own specification and ‘parameter’ estimates. The econometric Holy Grail of consistent and stable parameter-values is nothing but a dream.

A rigorous application of econometric methods in economics really presupposes that the phenomena of our real world economies are ruled by stable causal relations between variables.  Parameter-values estimated in specific spatio-temporal contexts are presupposed to be exportable to totally different contexts. To warrant this assumption one, however, has to convincingly establish that the targeted acting causes are stable and invariant so that they maintain their parametric status after the bridging. The endemic lack of predictive success of the econometric project indicates that this hope of finding fixed parameters is a hope for which there really is no other ground than hope itself.

The theoretical conditions that have to be fulfilled for econometrics to really work are nowhere even closely met in reality. Making outlandish statistical assumptions do not provide a solid ground for doing relevant social science and economics. Although econometrics has become the most used quantitative methods in economics today, it’s still a fact that the inferences made from them are as a rule invalid.

Econometrics is basically a deductive method. Given the assumptions, it delivers deductive inferences. The problem, of course, is that we will never completely know when the assumptions are right. Conclusions can only be as certain as their premises — and that also applies to econometrics.